import numpy as np
import pandas as pd
import seaborn as sb
import matplotlib.pyplot as plt
import scipy as sp
import copy
import os
directory = os.getcwd()
df = pd.read_csv(directory+"/results.csv")
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1350 entries, 0 to 1349 Data columns (total 17 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Round_name 1350 non-null object 1 Run_id 1350 non-null object 2 Version 1350 non-null object 3 Total_timespan 1350 non-null int64 4 Total_travel_time 1350 non-null int64 5 Average_travel_Time 1350 non-null float64 6 Max_Travel_time 1350 non-null int64 7 Total_#_cars 1350 non-null int64 8 #_finished 1350 non-null int64 9 Deadline Misses 1350 non-null int64 10 Deadline_overTime 1350 non-null int64 11 Time 1350 non-null float64 12 Teleport_Jam 1350 non-null int64 13 Teleport_Yield 1350 non-null int64 14 Teleport_Wrong_Lane 1350 non-null int64 15 #_Collisions 1350 non-null int64 16 #Emergency_stops 1350 non-null int64 dtypes: float64(2), int64(12), object(3) memory usage: 179.4+ KB
#Time_best = {}
Traffic_incidents = {}
Traffic_incidents['Micro'] = {}
Traffic_incidents['Micro']['teleports']=0
Traffic_incidents['Micro']['Collisions']=0
Traffic_incidents['Meso'] = {}
Traffic_incidents['Meso']['teleports']=0
Traffic_incidents['Meso']['Collisions']=0
Traffic_incidents['Macro'] = {}
Traffic_incidents['Macro']['teleports']=0
Traffic_incidents['Macro']['Collisions']=0
# Traffic_incidents['UH']
# Traffic_incidents['UH']['Meso']
# Traffic_incidents['UH']['Macro']
# Traffic_incidents['Random']['Micro']
# Traffic_incidents['Random']['Meso']
# Traffic_incidents['Random']['Macro']
# Traffic_incidents['Braess']['Micro']
# Traffic_incidents['Braess']['Meso']
# Traffic_incidents['Braess']['Macro']
# maps = {'UH':'4corners_neighborhoods.net.xml-test-',
# 'Random':'Random_English.net.xml-test_R-',
# 'Braess':'Braess_Homebrew_fixed1.net.xml-test-'}
methods = ['Micro','Meso','Macro']
#temp_name = base_name + str(x) + "-Iteration-"+str(y)
#print(temp_name)
#print(df['Deadline Misses'].loc[(df['Version']=='DUE.9.5')&(df['Round_name']==temp_name)])
c1 = df['Teleport_Jam'].loc[(df['Version']=='Micro-DUE.9.5')]
Traffic_incidents['Micro']['teleports'] +=sum(c1)
c1 = df['Teleport_Yield'].loc[(df['Version']=='Micro-DUE.9.5')]
Traffic_incidents['Micro']['teleports'] +=sum(c1)
c1 = df['Teleport_Wrong_Lane'].loc[(df['Version']=='Micro-DUE.9.5')]
Traffic_incidents['Micro']['teleports'] +=sum(c1)
c1 = df['Teleport_Jam'].loc[(df['Version']=='Meso-DUE.9.5')]
Traffic_incidents['Meso']['teleports'] +=sum(c1)
c1 = df['Teleport_Yield'].loc[(df['Version']=='Meso-DUE.9.5')]
Traffic_incidents['Meso']['teleports'] +=sum(c1)
c1 = df['Teleport_Wrong_Lane'].loc[(df['Version']=='Meso-DUE.9.5')]
Traffic_incidents['Meso']['teleports'] +=sum(c1)
c1 = df['Teleport_Jam'].loc[(df['Version']=='Macro-DUE.9.5')]
Traffic_incidents['Macro']['teleports'] +=sum(c1)
c1 = df['Teleport_Yield'].loc[(df['Version']=='Macro-DUE.9.5')]
Traffic_incidents['Macro']['teleports'] +=sum(c1)
c1 = df['Teleport_Wrong_Lane'].loc[(df['Version']=='Macro-DUE.9.5')]
Traffic_incidents['Macro']['teleports'] +=sum(c1)
c1 = df['#_Collisions'].loc[(df['Version']=='Micro-DUE.9.5')]
Traffic_incidents['Micro']['Collisions'] +=sum(c1)
c1 = df['#_Collisions'].loc[(df['Version']=='Meso-DUE.9.5')]
Traffic_incidents['Meso']['Collisions'] +=sum(c1)
c1 = df['#_Collisions'].loc[(df['Version']=='Macro-DUE.9.5')]
Traffic_incidents['Macro']['Collisions'] +=sum(c1)
plt.figure(figsize = (80,50))
plt.rc('font', size=150)
micro_t = Traffic_incidents['Micro']['teleports']
meso_t = Traffic_incidents['Meso']['teleports']
macro_t = Traffic_incidents['Macro']['teleports']
doot = plt.bar(0,micro_t)
doot = plt.bar(1,meso_t)
doot = plt.bar(2,macro_t)
name = ["Microscopic","Mesoscopic","Macroscopic"]
plt.xlabel('Simulation View')
plt.ylabel('Number of Teleports')
plt.title('Total number of teleports per method')
index = np.arange(3)
plt.xticks(ticks = index,labels = name,)
plt.text(-.15,micro_t+.1,micro_t)
plt.text(0.9,meso_t+1,meso_t)
plt.text(1.9,macro_t+1,macro_t)
plt.legend()
plt.show()
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
#Time_best = {}
Traffic_incidents = {}
Traffic_incidents['UH'] = {}
Traffic_incidents['UH']['Micro'] = {}
Traffic_incidents['UH']['Micro']['teleports']=0
Traffic_incidents['UH']['Micro']['Collisions']=0
Traffic_incidents['UH']['Meso'] = {}
Traffic_incidents['UH']['Meso']['teleports']=0
Traffic_incidents['UH']['Meso']['Collisions']=0
Traffic_incidents['UH']['Macro'] = {}
Traffic_incidents['UH']['Macro']['teleports']=0
Traffic_incidents['UH']['Macro']['Collisions']=0
Traffic_incidents['Braess'] = {}
Traffic_incidents['Braess']['Micro'] = {}
Traffic_incidents['Braess']['Micro']['teleports']=0
Traffic_incidents['Braess']['Micro']['Collisions']=0
Traffic_incidents['Braess']['Meso'] = {}
Traffic_incidents['Braess']['Meso']['teleports']=0
Traffic_incidents['Braess']['Meso']['Collisions']=0
Traffic_incidents['Braess']['Macro'] = {}
Traffic_incidents['Braess']['Macro']['teleports']=0
Traffic_incidents['Braess']['Macro']['Collisions']=0
Traffic_incidents['random'] = {}
Traffic_incidents['random']['Micro'] = {}
Traffic_incidents['random']['Micro']['teleports']=0
Traffic_incidents['random']['Micro']['Collisions']=0
Traffic_incidents['random']['Meso'] = {}
Traffic_incidents['random']['Meso']['teleports']=0
Traffic_incidents['random']['Meso']['Collisions']=0
Traffic_incidents['random']['Macro'] = {}
Traffic_incidents['random']['Macro']['teleports']=0
Traffic_incidents['random']['Macro']['Collisions']=0
# Traffic_incidents['UH']
# Traffic_incidents['UH']['Meso']
# Traffic_incidents['UH']['Macro']
# Traffic_incidents['Random']['Micro']
# Traffic_incidents['Random']['Meso']
# Traffic_incidents['Random']['Macro']
# Traffic_incidents['Braess']['Micro']
# Traffic_incidents['Braess']['Meso']
# Traffic_incidents['Braess']['Macro']
# maps = {'UH':'4corners_neighborhoods.net.xml-test-',
# 'Random':'Random_English.net.xml-test_R-',
# 'Braess':'Braess_Homebrew_fixed1.net.xml-test-'}
methods = {'Micro':'Micro-DUE.9.5','Meso':'Meso-DUE.9.5','Macro':'Macro-DUE.9.5'}
maps = {'UH':'4corners_neighborhoods.net.xml-test-',
'random':'Random_English.net.xml-test_R-',
'Braess':'Braess_Homebrew_fixed1.net.xml-test-'}
#temp_name = base_name + str(x) + "-Iteration-"+str(y)
#print(temp_name)
#print(df['Deadline Misses'].loc[(df['Version']=='DUE.9.5')&(df['Round_name']==temp_name)])
for x in maps.keys():
print(x)
for y in methods.keys():
for a in range(0,5):
for z in range(0,30):
temp_name = maps[x] + str(a) + "-Iteration-"+str(z)
c1 = df['Teleport_Jam'].loc[(df['Version']==methods[y])&(df['Round_name']==temp_name)]
#print(maps[x])
#print(c1)
Traffic_incidents[x][y]['teleports'] +=sum(c1)
c1 = df['Teleport_Yield'].loc[(df['Version']==methods[y])&(df['Round_name']==temp_name)]
Traffic_incidents[x][y]['teleports'] +=sum(c1)
c1 = df['Teleport_Wrong_Lane'].loc[(df['Version']==methods[y])&(df['Round_name']==temp_name)]
Traffic_incidents[x][y]['teleports'] +=sum(c1)
UH random Braess
for x in maps.keys():
plt.figure(figsize = (80,50))
plt.rc('font', size=150)
micro_t = Traffic_incidents[x]['Micro']['teleports']
meso_t = Traffic_incidents[x]['Meso']['teleports']
macro_t = Traffic_incidents[x]['Macro']['teleports']
print(micro_t)
print(macro_t)
print(meso_t)
doot = plt.bar(0,micro_t)
doot = plt.bar(1,meso_t)
doot = plt.bar(2,macro_t)
name = ["Microscopic","Mesoscopic","Macroscopic"]
plt.xlabel('Simulation View')
plt.ylabel('Number of Teleports')
plt.title('Total number of teleports for Network: '+ str(x))
index = np.arange(3)
plt.xticks(ticks = index,labels = name,)
plt.text(-.15,micro_t+.1,micro_t)
plt.text(0.9,meso_t+1,meso_t)
plt.text(1.9,macro_t+.05,macro_t)
plt.legend()
plt.show()
#break
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
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No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
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No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
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